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Airborne transmission is an important route of spread of viral diseases (e.g., COVID-19) inside the confined spaces. In this respect, computational fluid dynamics (CFD) emerged as a reliable and fast tool to understand the complex flow patterns in such spaces. Most of the recent studies, nonetheless, focused on the spatial distribution of airborne pathogens to identify the infection probability without considering the exposure time. This research proposes a framework to evaluate the infection probability related to both spatial and temporal parameters. A validated Eulerian-Lagrangian CFD model of exhaled droplets is first developed and then evaluated with an office case study impacted by different ventilation strategies (i.e., cross- (CV), single- (SV), mechanical- (MV) and no-ventilation (NO)). CFD results were analyzed in a bespoke code to calculate the tempo-spatial distribution of accumulated airborne pathogens. Furthermore, two indices of local and general infection risks were used to evaluate the infection probability of the ventilation scenarios. The results suggest that SV has the highest infection probability while SV and NO result in higher dispersions of airborne pathogens inside the room. Eventually, the time history of indices reveals that the efficiency of CV and MV can be poor in certain regions of the room.

Background Biofilm is a community of bacteria embedded in an extracellular matrix, which can colonize different human cells and tissues and subvert the host immune reactions by preventing immune detection and polarizing the immune reactions towards an anti-inflammatory state, promoting the persistence of biofilm-embedded bacteria in the host. Main body of the manuscript It is now well established that the function of immune cells is ultimately mediated by cellular metabolism. The immune cells are stimulated to regulate their immune functions upon sensing danger signals. Recent studies have determined that immune cells often display distinct metabolic alterations that impair their immune responses when triggered. Such metabolic reprogramming and its physiological implications are well established in cancer situations. In bacterial infections, immuno-metabolic evaluations have primarily focused on macrophages and neutrophils in the planktonic growth mode. Conclusion Based on differences in inflammatory reactions of macrophages and neutrophils in planktonic- versus biofilm-associated bacterial infections, studies must also consider the metabolic functions of immune cells against biofilm infections. The profound characterization of the metabolic and immune cell reactions could offer exciting novel targets for antibiofilm therapy.

Introduction Antibiotic-associated diarrhea (AAD) is a major hospital problem and a common adverse effect of antibiotic treatment. The aim of this study was to investigate the prevalence of the most important bacteria that cause AAD in hospitalized patients. Materials and methods PubMed, Web of Science and Scopus databases were searched using multiple relevant keywords and screening carried out based on inclusion/exclusion criteria from March 2001 to October 2021. The random-effects model was used to conduct the meta-analysis. Results Of the 7,377 identified articles, 56 met the inclusion criteria. Pooling all studies, the prevalence of Clostridioides (Clostridium) difficile, Clostridium perfringens, Klebsiella oxytoca, and Staphylococcus aureus as AAD-related bacteria among hospitalized patients were 19.6%, 14.9%, 27%, and 5.2%, respectively. The prevalence of all four bacteria was higher in Europe compared to other continents. The highest resistance of C. difficile was estimated to ciprofloxacin and the lowest resistances were reported to chloramphenicol, vancomycin, and metronidazole. There was no or little data on antibiotic resistance of other bacteria. Conclusions The results of this study emphasize the need for a surveillance program, as well as timely public and hospital health measures in order to control and treat AAD infections.

Pathogen droplets released from respiratory events are the primary means of dispersion and transmission of the recent pandemic of COVID-19. Computational fluid dynamics (CFD) has been widely employed as a fast, reliable, and inexpensive technique to support decision-making and envisage mitigatory protocols. Nonetheless, the airborne pathogen droplet CFD modeling encounters its limitations in the oversimplification of involved physics and the intensive computational demand. Moreover, uncertainties in the collected clinical data required to simulate airborne and aerosol transport such as droplets’ initial velocities, tempo-spatial profiles, release angle, and size distributions are broadly reported in the literature. Furthermore, there is a noticeable inconsistency around these collected data amongst many reported studies. Hence, this study aims to review the capabilities and limitations associated with CFD modeling. Setting the CFD models needs experimental data of respiratory flows such as velocity, particle size, and number distribution. Hence, we will also briefly review the experimental techniques used to measure the characteristics of airborne pathogen droplet transmissions together with their limitations and reported uncertainties. Moreover, the relevant clinical data related to pathogen transmission needed for postprocessing of CFD data and translating them to safety measures will also be reviewed. Eventually, we scrutinize the uncertainty and inconsistency of the existing clinical data available for airborne pathogen CFD analysis to pave a pathway toward future studies ensuing these identified gaps and limitations.